Asset allocators turn to plan universes to assess their performance relative to their peers. That same plan universe data also holds the potential to help them do more. What would you do if you received a sudden, huge donation to your endowment plan? Where would you invest? What would your peers with higher assets do? Or, what if your defined benefit plan were struggling to meet liquidity requirements? How would you get it back on track?
With the right data set, asset allocators can dig deeper into performance drivers and uncover insights to inform critical decisions that lie ahead. The use cases below demonstrate how peer comparisons can help you determine whether to stay the course, rebalance or even reconsider manager and product selections.
Use Case 1: Endowments and Foundations
Endowments and foundations operate under a different set of objectives when compared to retirement plans. Instead of covering pre-set liability obligations like pension and benefit payments, these non-profit organizations invest in making money and fund operational/capital projects and grants. To do so, they amass donations, manage the capital raised to generate returns, and draw down on the returns for designated projects. The donations often follow a set pattern driven by carefully orchestrated fundraising campaigns. While there are rules governing investment, withdrawal and usage, endowments and foundations do have the ability to take a longer-term view. They often have the flexibility to postpone projects as needed to ride out market fluctuations.
For endowments and foundations, peer-to-peer plan comparisons become particularly valuable when significant donations come in. Consider an endowment plan that has $250M in its plan. A major donor pledges another $125M. Suddenly the capital base will grow by 50 percent. The asset allocators can run new peer comparisons to see how funds in the $375M range allocate their investments—and how they perform. Or they may consider taking on some new investment strategies and explore how plans with similar profiles have been allocated and performed. A rich data set that enables them to control for size, allocation, performance and more can provide valuable data points that can help the endowment plan move forward most successfully with far more capital.
Like defined benefit plans, further detailed below, endowment plans benefit from rich peer universe comparisons. With Pensions & Investments posting blaring headlines on the performance of individual university endowments, the ability to articulate underlying drivers, such as differences in allocation, capital levels, investment rules, etc., is essential to the conversation—and to considering smart steps going forward.
Use Case 2: Defined Benefit Plans
For defined benefit plans, responsibility for plan liability lies with the plan sponsor. The plan must be sufficiently funded to cover its obligation to its members, and it must rebalance as necessary to meet this obligation. The liquidity requirements are clear—and the risks are as well. The PBGC (Pension Benefit Guaranty Corporation) monitors plans, sends out “early warnings” and may even step in if a plan fails to meet prescribed liquidity thresholds. From boards to employee members, plans also answer to a range of stakeholders. All of them expect their plans to operate under a clearly delineated investment policy for asset allocation, funding and returns.
Plans that come up short must return to alignment—but how? Comparing their plan to others of the same type and size, asset allocators may uncover clues to help get back on track. It’s a matter of controlling for different variables to help uncover the ones that matter. With a rich enough plan sponsor data set, asset allocators can drill down into peer comparisons to determine where differences lie. For example, low funding levels may necessitate a more aggressive allocation, whereas higher funding levels may allow a plan to de-risk via a more conservative allocation. Being able to ascertain how your peers in a similar situation are attacking these challenges is a crucial benefit of true peer benchmarking. Control for funding levels and asset allocation—as well as performance, type and size of plan—across peers is vital to gaining valuable insights. These insights can help determine what questions should be surfaced. With similar performance, the issue may be primarily managerial, whereas differences in allocation may point to a way forward in terms of rebalancing.
It’s also helpful to be able to view peer data trends over time. Plans at the same funding level today may be passing through that level on the way up or down. A true comparable will share the same trajectory, whereas a plan working its way back up may offer an example of how to right the ship. It can also be helpful to look at similar plans, in similar economic conditions, with similar funding challenges at an earlier point in time. What steps did they take to get back on track? Did the peer group show a shift in allocation? None of this is a crystal ball that will dictate exactly what to do, but it all fuels insight that can help asset allocators make more informed decisions.
Selecting Plan Universe Data
Choosing a plan universe provider is all about the data. How comprehensive is the data? How deeply can you drill down into performance drivers? How dependable is it? Is it drawn directly from a reliable source? How all-encompassing is it? What plan types, sizes and asset classes does it cover?
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